Making Biorisk Measurable: A Bayesian Framework for Laboratory Risk Management

Abstract

Purpose: Biosafety risk assessment traditionally relies on categorical scales embodied by the four World Health Organization (WHO) Risk Groups (RGs) and biocontainment levels. Mapping such categories to quantitative metrics is an open problem for the field: the classifications are too coarse for operational decision-making, yet strictly probabilistic language remains inaccessible to most safety professionals, laboratory managers, and decision-makers. Major Findings: To bridge these gaps, the present work develops a quantitative Bayesian framework for laboratory risk management that combines WHO RG classification as a prior with a Markov chain model of the incident–disaster escalation chain. Risk is reported on a log-risk scale that transforms multiplicative probabilities into additive quantities, mirroring the decibel scale in acoustics. The framework accommodates longitudinal updating with local incident data and quantifies the separate contributions of training, preventive maintenance, and inspection to system-level safety. Optionally, resource allocation recommendations providing auditable, evidence-based prioritization can be issued. The framework is illustrated on synthetic biosafety level-3 scenarios. Conclusions: The presented framework shifts the perspective of biorisk governance from static compliance assessment to dynamic risk and resource management.